Conventional image processing is any form of signal processing for which the input is an image, such as photographs or frames of video. The output of image processing can be either an image or a set of characteristics or parameters related to the image. Most image-processing techniques involve treating the image as a two-dimensional signal and applying standard signal-processing techniques to it.
In conventional image processing, image segmentation refers to the process of partitioning a digital image into multiple regions (such as multiple sets of pixels). After segmenting the image into different regions, the conventional image processing includes analyzing the segments to produce an output such as a focus value. According to certain conventional focus algorithms, a focus window is assumed to be the center of an image.
The goal of segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation can include locating objects and boundaries (lines, curves, etc.) in images. The result of image segmentation identifies a region located near the center of the image—where each pixel located in a particular region are similar with respect to some characteristic or computed property (such as color, intensity, or texture). However, adjacent regions include pixels that are significantly different with respect to the those characteristics.